62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
import matplotlib.pyplot as plt
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import seaborn as sns
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import pandas as pd
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# 数据准备：不同模型的 MSE 和 R² 值
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data = {
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'Model': [
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'1D-Linear Regression', '1D-Stochastic Gradient Descent', '1D-K-Nearest Neighbors',
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'1D-Decision Tree', '1D-Random Forest', '1D-XGBoost', '1D-Multi-layer Perceptron',
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'2D-Linear Regression', '2D-Stochastic Gradient Descent', '2D-K-Nearest Neighbors',
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'2D-Decision Tree', '2D-Random Forest', '2D-XGBoost', '2D-Multi-layer Perceptron',
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'3D-Stochastic Gradient Descent', '3D-K-Nearest Neighbors', '3D-Decision Tree',
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'3D-Random Forest', '3D-XGBoost', '3D-Multi-layer Perceptron', 'UniMol QSAR'
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],
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'MSE': [
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32.3949, 230009980374197965960989638656.0, 30.2081,
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27.7150, 26.5204, 27.7147, 143.3505,
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30.1093, 33.7336, 48.8179,
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30.2360, 28.7916, 30.2351, 30.1715,
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64.5768, 38.6921, 30.2360,
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30.8310, 30.2362, 29.9844, 59.7204
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],
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'R2': [
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0.6525, -2467672699617844819673481216.0, 0.6759,
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0.7027, 0.7155, 0.7027, -0.5379,
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0.6770, 0.6381, 0.4763,
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0.6756, 0.6911, 0.6756, 0.6763,
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0.3072, 0.5849, 0.6756,
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0.6692, 0.6756, 0.6783, 0.3593
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]
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}
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# 转换为 DataFrame
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df = pd.DataFrame(data)
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# 过滤掉负值
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df_filtered = df[(df['MSE'] >= 0) & (df['R2'] >= 0)]
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# 设置画布
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plt.figure(figsize=(14, 10))
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# 绘制 MSE 比较的条形图
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plt.subplot(2, 1, 1)
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sns.barplot(x='MSE', y='Model', data=df_filtered, palette='viridis')
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plt.title('Comparison of MSE across QSAR Models (Filtered)')
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plt.xlabel('Mean Squared Error')
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plt.ylabel('QSAR Model')
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# 绘制 R² 比较的条形图
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plt.subplot(2, 1, 2)
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sns.barplot(x='R2', y='Model', data=df_filtered, palette='plasma')
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plt.title('Comparison of R² across QSAR Models (Filtered)')
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plt.xlabel('R-squared')
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plt.ylabel('QSAR Model')
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# 调整布局以避免重叠
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plt.tight_layout()
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# 显示图表
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# plt.show()
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plt.savefig('qsar_r2_scores.png', dpi=300)
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